The forensic analysis of fibers is currently completely manual and therefore time consuming. The automation
of analysis steps can significantly support forensic experts and reduce the time, required for the investigation.
Moreover, a subjective expert belief is extended by objective machine estimation. This work proposes the pattern
recognition pipeline containing the digital acquisition of a fiber media, the pre-processing for fiber segmentation,
and the extraction of the distinctive characteristics of fibers. Currently, basic geometrical features like width,
height, area of optically dominant fibers are investigated. In order to support the automatic classification of
fibers, supervised machine learning algorithms are evaluated. The experimental setup includes a car seat and
two pieces clothing of a different fabric. As preliminary work, acrylic as synthetic and sheep wool as natural
fiber are chosen to be classified. While sitting on the seat, a test person leaves textile fibers. The test aims at
automatic distinguishing of clothes through the fiber traces gained from the seat with the help of adhesive tape.
The digitalization of fiber samples is provided by a contactless chromatic white light sensor. First test results
showed, that two optically very different fibers can be properly assigned to their corresponding fiber type. The
best classifier achieves an accuracy of 75 percent correctly classified samples for our suggested features.